Unaffirmative Actions: Lessons on Refusal, Racism, and Youth Research
Bibliographic record
Abstract
We are all girls of colour attending an independent secondary school in downtown Toronto, where we learn from a majority white teaching and guidance staff, despite having a racially diverse student and city population. We used our school as an example of what we view as a widespread problem, both in our personal experiences in Toronto and as researched throughout Canada and the United States: a lack of racial diversity in secondary school faculty. Using youth participatory action research methodologies, we set out to investigate the source of this problem at our school, but instead encountered refusal and evasion by school administration and teachers of colour. They appeared to use various defense tactics to avoid acknowledging racism in our society. We categorized the ways staff refused and evaded our study into three groups: dismissiveness, rationalization, and sugarcoating. Our study became an example of the difficulties of youth research and of trying to subvert constructs like the teacher-student hierarchy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.188 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.046 | 0.155 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".